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Document
Document

Historical Queries Along Multiple Lines of Time Evolution
Historical Queries Along Multiple Lines of Time Evolution

Paper - Springer
Paper - Springer

Data structures and algorithms for high
Data structures and algorithms for high

lec2
lec2

Amortization - Jeff Erickson
Amortization - Jeff Erickson

ViST: A Dynamic Index Method for Querying XML Data by Tree
ViST: A Dynamic Index Method for Querying XML Data by Tree

... Figure 5 shows an example of using a suffix-tree-like structure to index structure-encoded sequences for non-contiguous matching. We insert two sequences, Doc1 and Doc2 , into the suffix tree. Originally, the elements in the sequences represent nodes in the XML document trees, from which the sequenc ...
Path Minima Queries in Dynamic Weighted Trees
Path Minima Queries in Dynamic Weighted Trees

MASS: A Multi-Axis Storage Structure for Large XML Documents
MASS: A Multi-Axis Storage Structure for Large XML Documents

advanced algorithms
advanced algorithms

Operations on general linear lists
Operations on general linear lists

Ch-12. ppt
Ch-12. ppt

Protein Family Classification using Sparse Markov Transducers
Protein Family Classification using Sparse Markov Transducers

FR2611841189
FR2611841189

... become a crucial aspect of database research. Many indexing structures have been proposed to support fast access to multidimensional data in relational databases. Among these indexing structures, the R+-trees has attracted significant attention as the tree structure is regarded as one of the most pr ...
Data structure
Data structure

Linked Lists
Linked Lists

Lecture8KS
Lecture8KS

... Basic Operations How do we implement this using Java's language features? MAW: null is not such a great idea. In the OO framework everything should be a class, even an empty list. The root concept List appears in several incarnations: empty, with leading int, with leading list. ...
question-paper-Data-Structures-with-C-06cs35-10cs35
question-paper-Data-Structures-with-C-06cs35-10cs35

Cache-Oblivious B-Trees
Cache-Oblivious B-Trees

A Skiplist-Based Concurrent Priority Queue with Minimal Memory
A Skiplist-Based Concurrent Priority Queue with Minimal Memory

Binomial, Fibonacci, and Pairing Heaps
Binomial, Fibonacci, and Pairing Heaps

Heap Construction - University of South Carolina
Heap Construction - University of South Carolina

... Initialize the essentially complete binary tree with the given order of the n keys • Starting from the last parental node downto the first parental node, check whether H [i ]  max H [2i ], H [2i  1] • If not, swap parental and child keys to satisfy this requirement Note that when checking a cert ...
Conc-Trees for Functional and Parallel Programming
Conc-Trees for Functional and Parallel Programming

Skew Heaps
Skew Heaps

Document
Document

... first putting x into a new node and then inserting this node into the circular list pointed at by min. The operation is done in O(1) Time. • To combine two nonempty B-heaps, combine the top circular lists of each into a single circular list. • The new combined B-heap pointer is the min pointer of on ...
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Red–black tree

A red–black tree is a binary search tree with an extra bit of data per node, its color, which can be either red or black. The extra bit of storage ensures an approximately balanced tree by constraining how nodes are colored from any path from the root to the leaf. Thus, it is a data structure which is a type of self-balancing binary search tree.Balance is preserved by painting each node of the tree with one of two colors (typically called 'red' and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced the tree can become in the worst case. When the tree is modified, the new tree is subsequently rearranged and repainted to restore the coloring properties. The properties are designed in such a way that this rearranging and recoloring can be performed efficiently.The balancing of the tree is not perfect but it is good enough to allow it to guarantee searching in O(log n) time, where n is the total number of elements in the tree. The insertion and deletion operations, along with the tree rearrangement and recoloring, are also performed in O(log n) time.Tracking the color of each node requires only 1 bit of information per node because there are only two colors. The tree does not contain any other data specific to its being a red–black tree so its memory footprint is almost identical to a classic (uncolored) binary search tree. In many cases the additional bit of information can be stored at no additional memory cost.
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